def test_class(self):
import numpy as np
from kamrecsys.datasets import load_movielens_mini
from kamrecsys.tm.plsa_multi import EventScorePredictor
data = load_movielens_mini()
rcmdr = EventScorePredictor(tol=1e-8, k=2, random_state=1234)
self.assertDictEqual(
vars(rcmdr), {
'k': 2,
'tol': 1e-8,
'maxiter': 100,
'alpha': 1.0,
'random_state': 1234,
'_rng': None,
'iid': None,
'eid': None,
'n_objects': None,
'n_otypes': 0,
'n_score_levels_': 0,
'i_loss_': np.inf,
'f_loss_': np.inf,
'n_iter_': 0,
'pZ_': None,
'pYgZ_': None,
'pRgZ_': None,
'pXgZ_': None,
'n_events_': 0,
'n_users_': 0,
'n_items_': 0,
'_q': None,
'score_levels_': None,
'use_expectation': True
})
# import logging
# logging.getLogger('kamrecsys').addHandler(logging.StreamHandler())
rcmdr.fit(data)
self.assertAlmostEqual(rcmdr.i_loss_, 5.41836900049, delta=1e-5)
self.assertAlmostEqual(rcmdr.f_loss_, 5.17361298499, delta=1e-5)
assert_allclose(rcmdr.score_levels_, [1, 2, 3, 4, 5], rtol=1e-5)
# known user and item
self.assertAlmostEqual(rcmdr.predict((1, 7)),
3.64580117249,
delta=1e-5)
self.assertAlmostEqual(rcmdr.predict((1, 9)), 3.6587422493, delta=1e-5)
self.assertAlmostEqual(rcmdr.predict((5, 7)),
3.60707987724,
delta=1e-5)
self.assertAlmostEqual(rcmdr.predict((5, 9)),
3.62184516985,
delta=1e-5)
# known user and unknown item
self.assertAlmostEqual(rcmdr.predict((1, 11)),
3.66032199689,
delta=1e-5)
self.assertAlmostEqual(rcmdr.predict((5, 12)),
3.62387542269,
delta=1e-5)
# unknown user and known item
self.assertAlmostEqual(rcmdr.predict((3, 7)),
3.60821491793,
delta=1e-5)
self.assertAlmostEqual(rcmdr.predict((11, 9)),
3.62304301551,
delta=1e-5)
# unknown user and item
self.assertAlmostEqual(rcmdr.predict((3, 11)),
3.62507437787,
delta=1e-5)
x = np.array([[1, 7], [1, 9], [1, 11], [3, 7], [3, 9], [3, 11], [5, 7],
[5, 9], [5, 11]])
assert_allclose(rcmdr.predict(x), [
3.64580117249, 3.6587422493, 3.66032199689, 3.60821491793,
3.62304301551, 3.62507437787, 3.60707987724, 3.62184516985,
3.62387542269
],
rtol=1e-5)
rcmdr.use_expectation = False
assert_allclose(rcmdr.predict(x), [4., 5., 5., 4., 4., 4., 4., 4., 4.],
rtol=1e-5)
def test_class(self):
import numpy as np
from kamrecsys.datasets import load_movielens_mini
from kamrecsys.tm.plsa_multi import EventScorePredictor
data = load_movielens_mini()
rcmdr = EventScorePredictor(tol=1e-8, k=2, random_state=1234)
self.assertDictEqual(
vars(rcmdr),
{
"k": 2,
"tol": 1e-8,
"maxiter": 100,
"alpha": 1.0,
"random_state": 1234,
"_rng": None,
"iid": None,
"eid": None,
"n_objects": None,
"n_otypes": 0,
"n_score_levels_": 0,
"i_loss_": np.inf,
"f_loss_": np.inf,
"n_iter_": 0,
"pZ_": None,
"pYgZ_": None,
"pRgZ_": None,
"pXgZ_": None,
"n_events_": 0,
"n_users_": 0,
"n_items_": 0,
"_q": None,
"score_levels_": None,
"use_expectation": True,
},
)
# import logging
# logging.getLogger('kamrecsys').addHandler(logging.StreamHandler())
rcmdr.fit(data)
self.assertAlmostEqual(rcmdr.i_loss_, 5.41836900049, delta=1e-5)
self.assertAlmostEqual(rcmdr.f_loss_, 5.17361298499, delta=1e-5)
assert_allclose(rcmdr.score_levels_, [1, 2, 3, 4, 5], rtol=1e-5)
# known user and item
self.assertAlmostEqual(rcmdr.predict((1, 7)), 3.64580117249, delta=1e-5)
self.assertAlmostEqual(rcmdr.predict((1, 9)), 3.6587422493, delta=1e-5)
self.assertAlmostEqual(rcmdr.predict((5, 7)), 3.60707987724, delta=1e-5)
self.assertAlmostEqual(rcmdr.predict((5, 9)), 3.62184516985, delta=1e-5)
# known user and unknown item
self.assertAlmostEqual(rcmdr.predict((1, 11)), 3.66032199689, delta=1e-5)
self.assertAlmostEqual(rcmdr.predict((5, 12)), 3.62387542269, delta=1e-5)
# unknown user and known item
self.assertAlmostEqual(rcmdr.predict((3, 7)), 3.60821491793, delta=1e-5)
self.assertAlmostEqual(rcmdr.predict((11, 9)), 3.62304301551, delta=1e-5)
# unknown user and item
self.assertAlmostEqual(rcmdr.predict((3, 11)), 3.62507437787, delta=1e-5)
x = np.array([[1, 7], [1, 9], [1, 11], [3, 7], [3, 9], [3, 11], [5, 7], [5, 9], [5, 11]])
assert_allclose(
rcmdr.predict(x),
[
3.64580117249,
3.6587422493,
3.66032199689,
3.60821491793,
3.62304301551,
3.62507437787,
3.60707987724,
3.62184516985,
3.62387542269,
],
rtol=1e-5,
)
rcmdr.use_expectation = False
assert_allclose(rcmdr.predict(x), [4.0, 5.0, 5.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0], rtol=1e-5)
